Mohammad Waseem

Posted on Jan 31

Securing Test Environments: Eliminating Leaking PII with Rust and Open Source Tools

#rust #security #privacy

In many development cycles, test environments are critical for validating new features and integrations. However, they often introduce an insidious security risk: leaking Personally Identifiable Information (PII). As a Senior Architect, I’ve encountered this challenge firsthand—where sensitive data inadvertently propagates into test databases, logs, or mock data, posing compliance and privacy risks.

To address this, utilizing Rust not only provides performance and safety benefits but also integrates seamlessly with open source tools for a robust, scalable solution. In this article, I’ll walk through how to implement a scheme for scanning, detecting, and masking PII in test data, leveraging Rust's ecosystem.

The Core Problem

Before diving into the solution, it’s important to understand how PII leaks in test environments:

Copying production databases with real data
Logging systems capturing user information accidentally
Manual data creation or augmentation that includes sensitive fields

Automating detection and anonymization reduces human error and ensures compliance.

The Solution Approach

Our approach involves three main steps:

Detection: Identify PII in test data.
Masking: Obfuscate or pseudonymize the data.
Verification: Ensure PII is effectively anonymized before test data is used.

To implement this, I integrated several open source tools with Rust:

Rust regex for pattern matching.
Serde for data serialization/deserialization.
Walrus for efficient stream processing.

Implementation Details

Detection with Regex

For detection, we use regex patterns to identify common PII formats such as emails, phone numbers, and SSNs.

use regex::Regex;

fn detect_pii(text: &str) -> bool {
    let email_re = Regex::new(r"[\w.-]+@[\w.-]+\.[a-zA-Z]{2,}").unwrap();
    let ssn_re = Regex::new(r"\d{3}-\d{2}-\d{4}").unwrap();
    let phone_re = Regex::new(r"\+?\d{1,3}?[-.\s]?\(?(\d{3})\)?[-.\s]?(\d{3})[-.\s]?(\d{4})").unwrap();

    email_re.is_match(text) || ssn_re.is_match(text) || phone_re.is_match(text)
}

This function scans input data and flags sensitive fields.

Masking with Pseudonymization

Once detected, data masking is performed using simple pseudonymization functions.

use rand::{thread_rng, Rng};
use rand::distributions::Alphanumeric;

fn pseudonymize(input: &str) -> String {
    let mut rng = thread_rng();
    (0..12).map(|_| rng.sample(Alphanumeric) as char).collect()
}

This pseudonym replaces sensitive fields with randomly generated tokens, ensuring data can be used safely in testing.

Processing Data Streams

Using Walrus, data streams from logs or datasets are processed pipeline-style, ensuring minimal latency.

// Pseudo code for stream processing
fn process_stream(data_stream: impl Iterator<Item=String>) {
    for record in data_stream {
        if detect_pii(&record) {
            let masked_record = mask_pii(&record);
            store(masked_record);
        } else {
            store(record);
        }
    }
}

This pipeline ensures all test data is scrupulously sanitized.

Final Thoughts

Using Rust’s powerful ecosystem allows for efficient, reliable, and safe handling of PII in test environments. Combining regex detection, pseudonymization, and stream processing, teams can prevent leakage and ensure compliance with privacy standards like GDPR and CCPA.

Implementing these strategies as part of CI/CD pipelines ensures ongoing protection of sensitive data, maintaining trust and integrity in your testing environments.

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